The continued proliferation of information and information documents in the Information Age requires ever improved methods for the effective management, categorization and document retrieval. The ever-increasing size and complexity of information that can be retrieved through sources such as the World Wide Web, while bringing an amount of information to a user's fingertips previously unimagined, also brings a unique challenge to organizing the information in a useful way. Using the World Wide Web, an ordinary user may have the ability to, in some form or another, access a number approaching one billion documents.
Search engines exist to search Internet based information, as well as Intranet based information. Among the tasks often performed by such search engines, is to search a collection of documents (whether relatively small or gigantic) using key words or phrases or information categories. Forms of artificial intelligence may also be used to look for appropriate variations on the key words' or phrases relied upon by the user. Because there is no all-encompassing database that includes every document accessible via the World Wide Web, and because there is a great deal of linked information, the effective grouping of documents of interest to a particular user at a particular time is the subject of vigorous development efforts.
U.S. Pat. No. 6,038,574 issued to James E. Pitkow et al., and assigned to Xerox Corporation (the assignee of this Letters Patent) discloses methods and apparatuses for clustering documents and related subsets of documents (such as those which are accessed via hyperlinks) using co-citation analysis. The general approach of the Pitkow patent, which can be administered through search engines, is to:                [generate] a document collection; for each document, determine the frequency of linkage, i.e. the number of times it is linked to by another document in the collection[;] threshold the documents based on some minimum frequency of linkage[;] create a list of pairs of documents that are linked to by the same document so that each of the pairs of documents has a count of the number of times (the co-citation frequency) that they were both linked to by another document[;] and cluster pairs using a suitable co-citation clustering technique.The aforementioned patent is hereby incorporated by reference.        
As another example, U.S. Pat. No. 6,182,091 issued to James E. Pitkow and Peter L. Pirolli, and also assigned to Xerox Corporation discloses a method of clustering related documents by studying the link structure of the documents in a document collection. The approaches of the aforementioned patents are often used to form indexes presented to a user to help organize the information. For example, the index might purport to relay a degree of confidence that a particular document or a group of particular documents is related to the topic of information sought. The index might indicate how often the retrieved documents have been previously retrieved or accessed, giving a measure of the importance others have placed on particular documents.
A number of other techniques have been used to cluster related documents. Additional discussion and background appears in, for example, U.S. patent application Ser. No. 09/922,700 filed Aug. 7, 2001 by Gary M. Oosta for “Methods for Document Indexing and Analysis.”
In addition to finding documents on a particular subject, there is also sometimes a need to find commentators or authorities on particular subjects. For example, research paper and dissertation writers may wish to find articles of those eminent in particular fields, and further, may wish to discover some degree of information about the relative eminence of one author compared with another. As a further example, those seeking to employ expert witnesses may wish to do so at least partially based on the extend to which potential expert witnesses have published articles, books, papers, etc.
Notwithstanding the many approaches to clustering related documents, there currently exists no mechanized prior art approach to rank authors of documents so as to indicate from a search of documents, the degree to which particular authors may be considered subject matter experts (SMEs).